Effects of the deep learning-based super-resolution method on thermal image classification applications

Thermal imaging can be used in many sectors such as public security, health, and defense in image processing. However, thermal imaging systems are very costly, limiting their use, especially in the medical field. Also, thermal camera systems obtain blurry images with low levels of detail. Therefore, the need to improve their resolution has arisen. Here, super-resolution techniques can be a solution. Developments in deep learning in recent years have increased the success of super-resolution (SR) applications. This study proposes a new deep learning-based approach TSRGAN model for SR applications performed on a new dataset consisting of thermal images of premature babies. This dataset was created by downscaling the thermal images (ground truth) of premature babies as traditional SR studies. Thus, a dataset consisting of high-resolution (HR) and low-resolution (LR) thermal images were obtained. SR images created due to the applications were compared with LR, bicubic interpolation images, and obtained SR images using state-of-the-art models. The success of the results was evaluated using image quality metrics of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The results show that the proposed model achieved the second-best PSNR value and the best SSIM value. Additionally, a CNN-based classifier model was developed to perform task-based evaluation, and classification applications were carried out separately on LR, HR, and reconstructed SR image sets. Here, the success of classifying unhealthy and healthy babies was compared. This study showed that the classification accuracy of SR images increased by approximately 5% compared to the classification accuracy of LR images. In addition, the classification accuracy of SR thermal images approached the classification accuracy of HR thermal images by about 2%. Therefore, with the approach proposed in this study, it has been proven that LR thermal images can be used in classification applications by increasing their resolution. Thus, widespread use of thermal imaging systems with lower costs in the medical field will be achieved.

     

Optimal subset-difference broadcast encryption with free riders

Broadcast encryption (BE) deals with secure transmission of a message to a group of receivers such that only an authorized subset of receivers can decrypt the message. The transmission cost of a BE system can be reduced considerably if a limited number of free riders can be tolerated in the system. In this paper, we study the problem of how to optimally place a given number of free riders in a subset-difference (SD)-based BE system, which is currently the most efficient BE scheme in use and has also been incorporated in standards, and we propose a polynomial-time optimal placement algorithm and three more efficient heuristics for this problem. Simulation experiments show that SD-based BE schemes can benefit significantly from the proposed algorithms.     

Acoustic modes of self-excited oscillations in struts

The acoustic modes of self-excited oscillations are investigated numerically for two-dimensional generic struts. In profiles with short chord lengths, hydrodynamic instability is responsible for wake bifurcation and for generation of narrow band tones. However, it is found, another mechanism is the major factor for periodic oscillations in long profiles. Two points of wall contour develops an acoustic feedback between each other. These points are the inflection point and the trailing edge point. Consequently, limits of two different modes of periodic oscillations are outlined.Moreover, it is determined that acoustic radiation has dipole character which agrees well with the theory of sound radiation for acoustically compact surfaces.     

POYRAZ: CONTEXT-AWARE SERVICE SELECTION UNDER DECEPTION

The increasing number of service providers on the Web makes it challenging to select a provider for a specific service demand. Each service consumer has different expectations for a given service in different contexts, so the selection process should be consumer-oriented and context-dependent. Current approaches for service selection typically have consumers receive ratings of providers from other consumers, where the ratings reflect the consumers' overall subjective opinions. This may be misleading if consumers have different contexts and satisfaction criteria. In this paper, we propose that consumers objectively record their experiences, using an ontology to capture subtle details. This can then be interpreted by consumers according to their own criteria and contexts. We then integrate a method for addressing consumers who lie about their experiences, filtering them out during service selection. We demonstrate the value of our approach through experiments comparing our model with three recent rating-based service selection models. Our experiments show that using the proposed approach, service consumers can select the service providers for their needs more accurately even if the consumers have different criteria, they change the contexts of their service demands over time, or a significant portion of them are liars.     

An expert system based on S-transform and neural network for automatic classification of power quality disturbances

In this paper, an S-transform-based neural network structure is presented for automatic classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of classification. Features extracted by using the S-transform are applied as input to NN for automatic classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances.     

A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases

In this paper, a new feature selection method based on Association Rules (AR) and Neural Network (NN) is presented for the diagnosis of erythemato-squamous diseases. AR is used for reducing the dimension of erythemato-squamous diseases dataset and NN is used for efficient classification. The proposed AR+NN system performance is compared with that of other feature selection algorithms+NN. The dimension of input feature space is reduced from thirty four to twenty four by using AR. In test stage, 3-fold cross validation method is applied to the erythemato-squamous diseases dataset to evaluate the proposed system performances. The correct classification rate of proposed system is 98.61%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases.     

Surface–gas interaction effects on nanoscale gas flows

Molecular dynamics (MD) method is used to simulate shear driven argon gas flows in the early transition and free molecular flow regimes to investigate surface effects as a function of the surface–gas potential strength ratio (ε_wf/ε_ff). Results show a bulk flow region and a near wall region that extends three molecular diameters away from the surfaces. Within the near wall region the velocity, density, and shear stress distributions exhibit deviations from the kinetic theory predictions. Increased ε_wf/ε_ff results in increased gas density, leading toward monolayer adsorption on surfaces. The near wall velocity profile shows reduced gas slip, and eventually velocity stick with increased ε_wf/ε_ff. Using MD predicted shear stress values and kinetic theory, tangential momentum accommodation coefficients (TMAC) are calculated as a function of ε_wf/ε_ff, and TMAC values are shown to be independent of the Knudsen number. Presence of this near wall region breaks down the dynamic similarity between rarefied and nanoscale gas flows.     

基于多策略的维吾尔文网页识别方法

经过对大量维吾尔文网站的调查与分析,该文从多语种混合网页中针对维吾尔文网页识别进行了研究,这对维吾尔语信息处理工作起着关键作用。首先该文探讨了维吾尔文不规范网页的字符编码转换规则及原理,以此对不规范维吾尔文字符进行了相应的处理,之后介绍了基于修改的N-Gram方法和基于维吾尔语常用词特征向量的两种方法,其中后者融合了维吾尔文常用候选词语料库及向量空间模型(Vector Space Model)。使用三种不同类型的维吾尔文网页文本作为本研究的数据集,在此基础上验证了该文提出的网页识别方法,以及采用不同的方法进行了网页识别的实验。实验结果表明,基于N-Gram的方法对正文较长的新闻或论坛网页的识别性能最佳,反而基于常用词特征向量的方法对短文本的网页识别性能优越N-Gram。所提方法对维吾尔文网页识别的整体性能达到90%以上,并验证了这两种方法的有效性。     

Rainfall-Runoff Model Considering Microtopography Simulated in a Laboratory Erosion Flume

A comprehensive process-based rainfall-runoff model for simulating overland flow generated in rills and on interrill areas of a hillslope is evaluated using a laboratory experimental data set. For laboratory experiments, a rainfall simulator has been constructed together with a 6.50 m × 1.36 m erosion flume that can be given adjustable slopes changing between 5 % and 20 % in both longitudinal and lateral directions. The model is calibrated and validated using experimental data of simulated rainfall intensities between 45 and 105 mm/h. Results show that the model is capable of simulating the flow coming from the rill and interrill areas. It is found that most of the flow occurs in the form of rill flow. The hillslope-scale model can be used for better prediction of overland flow at the watershed-scale; it can also be used as a building block for an associated erosion and sediment transport model.     

Semantic web-based social network access control

The existence of online social networks that include person specific information creates interesting opportunities for various applications ranging from marketing to community organization. On the other hand, security and privacy concerns need to be addressed for creating such applications. Improving social network access control systems appears as the first step toward addressing the existing security and privacy concerns related to online social networks. To address some of the current limitations, we have created an experimental social network using synthetic data which we then use to test the efficacy of the semantic reasoning based approaches we have previously suggested.